NEW ERA OF BANKING PLATFORMS MIKHAIL KHASIN, SENIOR MANAGING DIRECTOR & HEAD OF CORE BANKING TRANSFORMATION PROGRAM SBERBANK
PARTNERS B2C E- LIFESTYLE COMMERCE Restaurants r e h Culture & leisure o t , d o E-HEALTH n o r F e o g i t n a e t r s o Electronics, clothing s Media (incl. p a social networks) REAL ESTATE s P n a r T P P h 2 a Payments P r Med. Institutions m Repair (furniture, a s TELECOM c r e i Purchase/lease decor), other e services f s s NEW ERA n a real estate r TECH PLAT T OF BANKING Trade financing Marketplace Loans of tariffs MNVO NOLO PLATFORMS FORM Materials, other LCA Consulting COMMERCE B2B E- Marketing, other Cars and equipment B Logistics Business ops. S Risk-management/ 2 E Agriculture B C ratings I V R E S PARTNERS
ANY ECOSYSTEM BASED ON TECHNOLOGICAL PLATFORM Hundreds millions Petabytes Hundreds of thousands clients of data transactions per second
INNOVATIONS AS DRIVER FOR UNIQUE PHENOMENON IN GLOBAL ECONOMY 175 000 175 000 120,7 bln. 2016 2015 140 000 85 900 91,2 bln. 2014 80 000 38 000 57,1 bln. 2013 42 000 15 000 35 bln 2012 14 000 3 850 19,1 bln. 2011 3 200 1 200 5,31 bln. 2010 1 000 500 1,94 bln. 2009 400 200 590 mln. Number of orders Number of payments Total amount of deals per second per second (yuan)
NEW GENERATION OF BANKING PLATFORM Client’s request EXTERNAL SITES OMNI CHANNEL Client session data FRONT END BUSINESS- All active API Activities in operations HUB Next social Best Offer networks Client PRODUCT All active Service Profile products FACTORIES DATA Archive FACTORIES of data to PUBLIC a depth of Information storing CLOUD at least Corporate memory (Hadoop) 15 years Bank Systems Platform replica External Sources Replicas Реплики А C Банка (archive of the platform) Replicas External Data Warehouse Profile Client History Detailed data model Analytics Area Client Analytics Behavior Modeling Needs Forecast EXTERNAL ANALYTICS Data Marts Analytical applications Models
COMPARISON OF THE TARGET SBERBANK PLATFORM CLUSTER WITH THE LARGEST SUPERCOMPUTERS OF THE WORLD* Sberbank Amazon Web Services National Center for Alibaba MIT, Lincoln Laboratory Moscow State University - (Russia) (USA) Atmospheric Research (China) (USA) Research Computing (NCAR) Center (USA) (Russia) Sberbank’s Platform Amazon EC2 C3 Cheyenne – Lenovo ThinkServer TX-Green - S7200AP Lomonosov 2 – System Cluster Instance cluster SGI ICE XA RD650 Cluster T-Platform A-Class Cluster Cores 56,000 26,496 144,900 84,000 41,472 42,688 3,377 1,500 Nodes 2,000 880 4,032 1,472 Theoretical Peak 2,150 593.5 5,332.3 3,360 1,725.23 2,102 (Rpeak), TFlop/s Memory, TB 1,536 103.5 198 218.75 121.5 92 * Data from worldwide TOP500 Supercomputer List (June 2017)
ARTIFICIAL INTELLIGENCE… — it’s been a long time since it ceased to be a science fiction and has became something we carry in our own pockets daily Apple’s Siri, Android’s Google Now, Yandex Alice, Personal Financial Assistants and other apps facilitate a brand new level of rendering information and financial services. Weekly the data-technology market brings new features enabling to propel AI even further across the industry. AI proved to be extremely sought after all the way from successful local business solutions to becoming a global financial trend as well as future banking cluster. Business models, processes, risks and experience are geared towards the general transformation wave.
BY 2020, THE MAJORITY OF NON-ROUTINE CAREER PATHS WILL BE AFFECTED BY SMART MACHINES “83% of professions paid less than $20 per hour will be taken by robots”. — Council of Economic Probability of automation by a profession’s median hourly wage Advisors, USA
KEY TRENDS IN AI ENGAGING FOR BANKING Chat bots Roboadvising Personalized offers Internet of Things Anti-fraud Operational efficiency
CHAT BOTS AND ROBO-ADVISING AI in banks. Key trends (1/6) � Rendering information on products & services � Provision of contact details � Payment transaction posting � Financial advising for clients
ROBO-ADVISORS AS A PROMISING AI APPLICATION: CASE STUDY AI in banks. Key trends (2/6) � Robo-advising has become an alternative financial consulting service provider on banking issues as well as specific Estimated U.S. Robo-advisors assets under management purchases and other monetary on-line transactions. ($ trillions) � Robo-advisors offer substantial advantages in on-line trading. First and foremost, this is due to single-click applications, account creation in a real time mode, monitoring, latest news and ability to process multiple deals at once. The brokers disseminated across social media improve data accessibility and comprehensiveness, and make communication with clients to be more targeted and easy job. � Automation enables to provide information in 24/7 mode in a 2016E 2017E 2018E 2019E 2020E less costly manner. Robo-advisors can be made accessible Growth due to invested assets (cash, bank deposits) either via your desktop or as a mobile app acting as portfolio Growth due to non-invested assets managers that are capable to identify risks and devise (Credit risk instruments, stock and mutual funds) streamlined investment strategy. Source : A.T.Kearney simulation model
PERSONALIZED OFFERS AND IMPROVED LOYALTY AI in banks. Key trends (3/6) � Recommending banking products and purchases (loyalty programs by different retailers) inter alia – relying upon client’s info from social media � Identifying the existing client’s B2B network and providing recommendations on engaging with new counterparties � Simulating financial risks for small businesses (default, cash deficiency etc.) in a real time mode; recommending new target strategies and products
IOT (INTERNET OF THINGS) AI in banks. Key trends (4/6) � Management and tracking of the leased assets � Smart insurance services for retail clients (health coverage, auto-loans etc.) � Smart Home + Daily Shopping: means ordering, public utility bills payment, TV content subscription � Banking of Things: transfer the payments function from people to devices (e.g., cars pay for gas, parking and using of toll roads) It is expected that the number of IoT-connections will grow by 23% annually within 2015 to 2021. IoT devices will encompass more than 16 billion out of 28 billion connected objects by the end of the projected period.
ANTI-FRAUD. INSIDE AND OUTSIDE THREATS AI in banks. Key trends (5/6) � Attributes hinting that a credit card is used by an authorized person � Attributes of so called “droppers” identified based on specificity of credits and transactions via online-bank and ATMS � Identifying fraudulent salary projects (loans, cash-pull) � Identifying unauthorized debit transactions from client’s accounts and cards � Errors in parameterization of the Bonus programs on credit cards resulting in unjustified mark-ups and loss & damage � Cash-pull schemes, including via online-bank and credit cards � Abuse in conversion transactions for both retail and corporate entities � Unauthorized connections of online-bank to client’s accounts and credit cards issuing without the knowledge of the client � Unauthorized limit increase for credit cards
OPERATIONAL EFFICIENCY AI in banks. Key trends (6/6) � Identifying deviations in transaction execution and automatic correction hereof � Natural Language Processing – algorithms for analysis and generation of legal claims � Monitoring and prediction of infrastructure failure (ATMs, IT-resources) � Streamlining of cash flow cycle in cash departments and ATMs. Streamlining of collection services � Optimization of recruitment and hiring processes (CV review and initial screening) � Speech analytics in a real time mode for call centres and branches (consultation quality management)
MACHINE LEARNING MECHANISMS � Identification of bottlenecks in transaction processing � Identification of root causes behind exceptions that occur upon documents execution and categorization thereof � Identification of major user’s mistakes in the system � Analysis of the system users & clients activities. Predicting the load on the platform � Analysis of client's product preferences and anticipating future actions � Best personal offer
Recommend
More recommend
